def discretize(X, method="ef", nbins=None):
"""
Discretize `X`
Parameters
----------
bins : int, optional
Number of bins. Default is floor(sqrt(N))
method : string
"ef" is equal-frequency binning
"ew" is equal-width binning
Examples
--------
"""
nobs = len(X)
if nbins == None:
nbins = np.floor(np.sqrt(nobs))
if method == "ef":
discrete = np.ceil(nbins * stats.rankdata(X)/nobs)
if method == "ew":
width = np.max(X) - np.min(X)
width = np.floor(width/nbins)
svec, ivec = stats.fastsort(X)
discrete = np.zeros(nobs)
binnum = 1
base = svec[0]
discrete[ivec[0]] = binnum
for i in range(1,nobs):
if svec[i] < base + width:
discrete[ivec[i]] = binnum
else:
base = svec[i]
binnum += 1
discrete[ivec[i]] = binnum
return discrete
def discretize(X, method="ef", nbins=None):
"""
Discretize `X`
Parameters
----------
bins : int, optional
Number of bins. Default is floor(sqrt(N))
method : string
"ef" is equal-frequency binning
"ew" is equal-width binning
Examples
--------
"""
nobs = len(X)
if nbins is None:
nbins = np.floor(np.sqrt(nobs))
if method == "ef":
discrete = np.ceil(nbins * stats.rankdata(X)/nobs)
if method == "ew":
width = np.max(X) - np.min(X)
width = np.floor(width/nbins)
svec, ivec = stats.fastsort(X)
discrete = np.zeros(nobs)
binnum = 1
base = svec[0]
discrete[ivec[0]] = binnum
for i in range(1,nobs):
if svec[i] < base + width:
discrete[ivec[i]] = binnum
else:
base = svec[i]
binnum += 1
discrete[ivec[i]] = binnum
return discrete
print phasetemp
phase.append(phasetemp)
print '...Done!\n'
head = pf.getheader(ff[0])
start = head['CRVAL1']
step = head['CDELT1']
length = head['NAXIS1']
x = start + pl.arange(0,length)*step
hi = x > 6500
low = x < 6625
xx = hi*low
# now get the phases, sort them and find the order of numbers.
phase,argsort = stats.fastsort(pl.array(phase))
im = pl.array([pf.getdata(ff[i])[xx]-pl.median(pf.getdata(ff[i])[xx]) for i in argsort])
print im
#im = pl.array([allspec[i].count for i in range(len(allspec))])
# phases are not equispaced, need to interpolate im on to regular phase grid.
#l,w = pl.shape(im)
#x = pl.linspace(0.0,1.0,l)
#im2 = 1.0*pl.zeros((l,w))
# start with lpDNO @ 829 c/d. check if this is the actual frequency
# calculate phase for every spectrum and assign number to it
#period = 1.0/3627.965
#t0 = float(pf.getheader(ff[0])['HJD'])
print 'Calculating phase...\n'
for i in range(len(ff[:-2])):
temp = ((float(pf.getheader(ff[i])['HJD']) - T0)/P)
phase.append(temp)
print '...Done!\n'
# now get the phases, sort them and find the order of numbers.
phase = pl.array(phase)
phase2 = (pl.array(phase) - phase[0]) / P2
phase2,argsort = stats.fastsort(pl.array(phase2))
# speed of light in km/s
c = 2.99792458e5
v = 1500.0
for i in ff:
# subtract average from spectrum
data = pf.getdata(i)# - ave
head = pf.getheader(i)
# write average subtracted spectrum to new fits file
#pf.writeto('avesub%s'%i,data=data,header=head)
pl.subplot(221)
pl.plot(pl.array(t-int(t[0])),pl.array(big))
ylo,yhi = pl.ylim()
pl.ylim(yhi,ylo)
#pl.xlim(0.3,0.6)
pl.title('Spectrum lightcurve')
pl.xlabel('HJD')
pl.ylabel('Total spectrum Mag')
pl.subplot(222)
f,a = ast.signal.dft(t,big,3000,3934,0.1)
pl.title('Spectrum periodogram')
pl.xlabel('Frequency [Cycles/Day]')
pl.ylabel('Amplitude [mag]')
pl.plot(f,a,'g-')
sorted,argsort = sci.fastsort(a)
print 'Max amplitude of %s at %s cycles/day' % (sorted[-1],f[argsort[-1]])
pl.subplot(223)
pl.plot(x,y,'.')
pl.title('Photometry lightcurve')
ylo,yhi = pl.ylim()
pl.ylim(yhi,ylo)
pl.xlabel('HJD')
pl.ylabel('Relative Magnitude')
pl.subplot(224)
f,a = ast.signal.dft(x,y,0,3934,0.1)
pl.title('Photometry periodogram')
pl.xlabel('Frequency [Cycles/Day]')
pl.ylabel('Amplitude [mag]')
pl.plot(f,a,'g-')
temp.append(date)
temp.append(phase)
temp.append(flux)
pl.save('HeIIlightcurve.dat',pl.array(temp).transpose())
lt = phase < 7.9
date = date[lt]
flux = pl.array(flux)[lt]
phase = pl.array(phase)[lt]
# flatten lightcurve using fourier method
fft = sci.fft(flux)
fft[0:4] = 0.0
fft[-4:] = 0.0
flux = sci.ifft(fft)
f,a = ast.signal.dft(date,flux,0,4000,1)
pl.figure()
pl.subplot(211)
pl.plot(phase,flux)
#pl.ylim(-5e-8,5e-8)
pl.subplot(212)
pl.plot(f,a)
sort,argsort = stats.fastsort(a)
print 'Max amplitude of %s at %s' % (a[argsort[-1]],f[argsort[-1]])
#print 'Second highest amplitude of %s at %s' % (a[argsort[-2]],f[argsort[-2]])
pl.show()
cof = pl.polyfit(x_bins[i]-xave,y_bins[i],3)
z_bins.append(pl.polyval(cof,x_bins[i]-xave))
f = string.split(raw_input('Start End freq : '))
f0 = float(f[0])
f1 = float(f[1])
print f0,f1
# now do fourier transform on every bins and give stats
ft_bins = [ast.signal.dft(x_bins[i],y_bins[i]-z_bins[i],f0,f1,1) for i in range(n)]
#print pl.shape(ft_bins)
print '\nBin\t','Amp\t\t\t','F c/d\t\t','F(s)\n\n'
for i in range(n):
sort, argsort = stats.fastsort(ft_bins[i][1])
amp = ft_bins[i][1][argsort[-1]]
freq = ft_bins[i][0][argsort[-1]]
print i,'\t',amp,'\t',freq,'\t',86400.0/freq
print '\n\n\n'
# make plots
pl.figure(figsize=(9,12))
for i in range(n):
plots = '%s1%s' % (n,i+1)
pl.subplot(plots)
pl.plot(ft_bins[i][0],ft_bins[i][1],'k')
pl.figure(figsize=(9,12))